Predictive models are used in a wide variety of contexts to predict the probability of an outcome. For example, a predictive model may be used to predict whether a previously unobserved unit of data (such as a record in a database) represents information associated with a particular outcome. In the context of healthcare, for example, predictive models may be used to predict whether data associated with a particular patient (such as data representing recent complaints of the patient) indicate that the patient is likely to experience a particular outcome, such as a readmission to a hospital. Although many tools exist to enable users to build predictive models, such tools require their users to have a sophisticated understanding of statistics.
What is needed, therefore, are tools that enable users who lack detailed knowledge of statistics to quickly and easily generate and validate predictive models against data, such as healthcare data (e.g., a free-text clinical documentation dataset).